OpenRaiser/Intern-Atlas huggingface dataset:
Performance Analysis of Inband FD-D2D Communications with Imperfect SI Cancellation for Wireless Video Distribution
Venue: arXiv cs.IT 2017
Year: 2017

Tremendous growing demand for high data rate services is the main driver for increasing traffic in wireless cellular networks. Device-to-Device (D2D) communications have recently been proposed to offload data via direct communications by bypassing cellular base stations (BSs). Such an offloading schemes increase capacity and reduce end-to-end delay in cellular networks and help to serve the dramatically increasing demand for high data rate. In this paper, we aim to analyze inband fullduplex (FD) D2D performance for the wireless video distribution by considering imperfect self-interference (SI) cancellation. Using tools from stochastic geometry, we analyze outage probability and spectral efficiency. Analytic and simulation results are used to demonstrate achievable gain against its half-duplex (HD) counterpart.
Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning
Venue: arXiv cs.CL 2017
Year: 2017

Human conversation is inherently complex, often spanning many different topics/domains. This makes policy learning for dialogue systems very challenging. Standard flat reinforcement learning methods do not provide an efficient framework for modelling such dialogues. In this paper, we focus on the under-explored problem of multi-domain dialogue management. First, we propose a new method for hierarchical reinforcement learning using the option framework. Next, we show that the proposed architecture learns faster and arrives at a better policy than the existing flat ones do. Moreover, we show how pretrained policies can be adapted to more complex systems with an additional set of new actions. In doing that, we show that our approach has the potential to facilitate policy optimisation for more sophisticated multi-domain dialogue systems.
Relative and Mean Motions of Multi-Machine Power Systems in Classical Model
Venue: arXiv cs.SY 2017
Year: 2017

It is well-known that in an m-machine power system where each machine is represented by a second-order differential equation, the Jacobian of the system equation contains (m-1) pairs of conjugate eigenvalues and two real eigenvalues, including at least one zero. This letter proves that under the uniform damping condition, the dynamics associated with the two real eigenvalues do not have any impact on the dynamics associated with those complex eigenvalues. This conclusion is important to justify the use of the relative motions or center-of-inertia (COI) coordinate to analyze the rotor angle stability in a multi-machine power system.
Understanding the Computational Requirements of Virtualized Baseband Units using a Programmable Cloud Radio Access Network Testbed
Venue: arXiv cs.NI 2017
Year: 2017

Cloud Radio Access Network (C-RAN) is emerging as a transformative architecture for the next generation of mobile cellular networks. In C-RAN, the Baseband Unit (BBU) is decoupled from the Base Station (BS) and consolidated in a centralized processing center. While the potential benefits of C-RAN have been studied extensively from the theoretical perspective, there are only a few works that address the system implementation issues and characterize the computational requirements of the virtualized BBU. In this paper, a programmable C-RAN testbed is presented where the BBU is virtualized using the OpenAirInterface (OAI) software platform, and the eNodeB and User Equipment (UEs) are implemented using USRP boards. Extensive experiments have been performed in a FDD downlink LTE emulation system to characterize the performance and computing resource consumption of the BBU under various conditions. It is shown that the processing time and CPU utilization of the BBU increase with the channel resources and with the Modulation and Coding Scheme (MCS) index, and that the CPU utilization percentage can be well approximated as a linear increasing function of the maximum downlink data rate. These results provide real-world insights into the characteristics of the BBU in terms of computing resource and power consumption, which may serve as inputs for the design of efficient resource-provisioning and allocation strategies in C-RAN systems.
Social Events in a Time-Varying Mobile Phone Graph
Venue: arXiv cs.SI 2017
Year: 2017

The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.
Multi-frame image super-resolution with fast upscaling technique
Venue: arXiv cs.CV 2017
Year: 2017

Multi-frame image super-resolution (MISR) aims to fuse information in low-resolution (LR) image sequence to compose a high-resolution (HR) one, which is applied extensively in many areas recently. Different with single image super-resolution (SISR), sub-pixel transitions between multiple frames introduce additional information, attaching more significance to fusion operator to alleviate the ill-posedness of MISR. For reconstruction-based approaches, the inevitable projection of reconstruction errors from LR space to HR space is commonly tackled by an interpolation operator, however crude interpolation may not fit the natural image and generate annoying blurring artifacts, especially after fusion operator. In this paper, we propose an end-to-end fast upscaling technique to replace the interpolation operator, design upscaling filters in LR space for periodic sub-locations respectively and shuffle the filter results to derive the final reconstruction errors in HR space. The proposed fast upscaling technique not only reduce the computational complexity of the upscaling operation by utilizing shuffling operation to avoid complex operation in HR space, but also realize superior performance with fewer blurring artifacts. Extensive experimental results demonstrate the effectiveness and efficiency of the proposed technique, whilst, combining the proposed technique with bilateral total variation (BTV) regu-larization, the MISR approach outperforms state-of-the-art methods.
Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation
Venue: arXiv cs.CV 2017
Year: 2017

Recent work in computer vision has yielded impressive results in automatically describing images with natural language. Most of these systems generate captions in a sin- gle language, requiring multiple language-specific models to build a multilingual captioning system. We propose a very simple technique to build a single unified model across languages, using artificial tokens to control the language, making the captioning system more compact. We evaluate our approach on generating English and Japanese captions, and show that a typical neural captioning architecture is capable of learning a single model that can switch between two different languages.
Fast Load Balancing Approach for Growing Clusters by Bioinformatics
Venue: arXiv cs.DC 2017
Year: 2017

This paper presents Fast load balancing technique inspired by Bioinformatics is a special case to assign a particular patient with a specialist physician cluster at real time. The work is considered soft presentation of the Gaussian mixture model based on the extracted features supplied by patients. Based on the likelihood ratio test, the patient is assigned to a specialist physician cluster. The presented algorithms efficiently handle any size and any numbers of incoming patient requests and rapidly placed them to the specialist physician cluster. Hence it smoothly balances the traffic load of patients even at a hazard situation in the case of natural calamities. The simulation results are presented with variable size of specialist physician clusters that well address the issue for randomly growing patient size.
Making visible the invisible through the analysis of acknowledgements in the humanities
Venue: arXiv cs.DL 2017
Year: 2017

Purpose: Science is subject to a normative structure that includes how the contributions and interactions between scientists are rewarded. Authorship and citations have been the key elements within the reward system of science, whereas acknowledgements, despite being a well-established element in scholarly communication, have not received the same attention. This paper aims to put forward the bearing of acknowledgements in the humanities to bring to the foreground contributions and interactions that, otherwise, would remain invisible through traditional indicators of research performance. Design/methodology/approach: The study provides a comprehensive framework to understanding acknowledgements as part of the reward system with a special focus on its value in the humanities as a reflection of intellectual indebtedness. The distinctive features of research in the humanities are outlined and the role of acknowledgements as a source of contributorship information is reviewed to support these assumptions. Findings: Peer interactive communication is the prevailing support thanked in the acknowledgements of humanities, so the notion of acknowledgements as super-citations can make special sense in this area. Since single-authored papers still predominate as publishing pattern in this domain, the study of acknowledgements might help to understand social interactions and intellectual influences that lie behind a piece of research and are not visible through authorship. Originality/value: Previous works have proposed and explored the prevailing acknowledgement types by domain. This paper focuses on the humanities to show the role of acknowledgements within the reward system and highlight publication patterns and inherent research features which make acknowledgements particularly interesting in the area as reflection of the socio-cognitive structure of research.
SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning
Venue: arXiv cs.CV 2017
Year: 2017

It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers. Therefore, several Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robustness of AdaBoost by replacing the exponential loss with some designed robust loss functions. In this work, we present a new way to robustify AdaBoost, i.e., incorporating the robust learning idea of Self-paced Learning (SPL) into Boosting framework. Specifically, we design a new robust Boosting algorithm based on SPL regime, i.e., SPLBoost, which can be easily implemented by slightly modifying off-the-shelf Boosting packages. Extensive experiments and a theoretical characterization are also carried out to illustrate the merits of the proposed SPLBoost.
THUMT: An Open Source Toolkit for Neural Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

This paper introduces THUMT, an open-source toolkit for neural machine translation (NMT) developed by the Natural Language Processing Group at Tsinghua University. THUMT implements the standard attention-based encoder-decoder framework on top of Theano and supports three training criteria: maximum likelihood estimation, minimum risk training, and semi-supervised training. It features a visualization tool for displaying the relevance between hidden states in neural networks and contextual words, which helps to analyze the internal workings of NMT. Experiments on Chinese-English datasets show that THUMT using minimum risk training significantly outperforms GroundHog, a state-of-the-art toolkit for NMT.
Wireless Optogenetic Nanonetworks: Device Model and Charging Protocols
Venue: arXiv cs.ET 2017
Year: 2017

In recent years, numerous research efforts have been dedicated towards developing efficient implantable devices for brain stimulation. However, there are limitations and challenges with the current technologies. Firstly, the stimulation of neurons currently is possible through implantable electrodes but limited to a population of neurons. Secondly, a major hurdle lies in developing miniature devices that can last for a lifetime in the patient's brain. In parallel, Optogenetics has emerged proposing the stimulation of neurons using light by means of optical fibers inserted through the skull. Many challenges are thus introduced in terms of suitability to patient's lifestyle and biocompatibility. We have recently proposed the concept of wireless optogenetic nanonetworking devices (WiOptND), addressing these long-term deployment problems, and at the same time targeting single neuron stimulation [1]. The WiOptND is equipped with a miniature LED that is able to stimulate a genetically engineered neuron while harvesting energy from ultrasonic vibrations. This paper investigates how light propagates in the brain tissue, and based on the power required to emit sufficient intensity for stimulation, an energy harvesting circuitry is designed. A number of charging protocols are also proposed to maximize energy efficiency while ensuring minimum number of neural spike misfirings. These protocols include the Charge and Fire, the Predictive Sliding Detection Window, and its variant Markov-Chain based Time-Delay Patterns. Simulation results show the drop of stimulation ratio for 25% and more stable trend in its efficiency ratio are exhibited on Markov-Chain based Time-Delay Patterns compared to Change and Fire. The results show the feasibility of utilizing WiOptND for long-term implants, and a new direction towards precise stimulation of neurons in the cortical columns of the brain.
Understanding Software in Research: Initial Results from Examining Nature and a Call for Collaboration
Venue: arXiv cs.SE 2017
Year: 2017

This lightning talk paper discusses an initial data set that has been gathered to understand the use of software in research, and is intended to spark wider interest in gathering more data. The initial data analyzes three months of articles in the journal Nature for software mentions. The wider activity that we seek is a community effort to analyze a wider set of articles, including both a longer timespan of Nature articles as well as articles in other journals. Such a collection of data could be used to understand how the role of software has changed over time and how it varies across fields.
Reputation blackboard systems
Venue: arXiv cs.DC 2017
Year: 2017

Blackboard systems are motivated by the popular view of task forces as brainstorming groups in which specialists write promising ideas to solve a problem in a central blackboard. Here we study a minimal model of blackboard system designed to solve cryptarithmetic puzzles, where hints are posted anonymously on a public display (standard blackboard) or are posted together with information about the reputations of the agents that posted them (reputation blackboard). We find that the reputation blackboard always outperforms the standard blackboard, which, in turn, always outperforms the independent search. The asymptotic distribution of the computational cost of the search, which is proportional to the total number of agent updates required to find the solution of the puzzle, is an exponential distribution for those three search heuristics. Only for the reputation blackboard we find a nontrivial dependence of the mean computational cost on the system size and, in that case, the optimal performance is achieved by a single agent working alone, indicating that, though the blackboard organization can produce impressive performance gains when compared with the independent search, it is not very supportive of cooperative work.
Index Search Algorithms for Databases and Modern CPUs
Venue: arXiv cs.DB 2017
Year: 2017

Over the years, many different indexing techniques and search algorithms have been proposed, including CSS-trees, CSB+ trees, k-ary binary search, and fast architecture sensitive tree search. There have also been papers on how best to set the many different parameters of these index structures, such as the node size of CSB+ trees. These indices have been proposed because CPU speeds have been increasing at a dramatically higher rate than memory speeds, giving rise to the Von Neumann CPU--Memory bottleneck. To hide the long latencies caused by memory access, it has become very important to well-utilize the features of modern CPUs. In order to drive down the average number of CPU clock cycles required to execute CPU instructions, and thus increase throughput, it has become important to achieve a good utilization of CPU resources. Some of these are the data and instruction caches, and the translation lookaside buffers. But it also has become important to avoid branch misprediction penalties, and utilize vectorization provided by CPUs in the form of SIMD instructions. While the layout of index structures has been heavily optimized for the data cache of modern CPUs, the instruction cache has been neglected so far. In this paper, we present NitroGen, a framework for utilizing code generation for speeding up index traversal in main memory database systems. By bringing together data and code, we make index structures use the dormant resource of the instruction cache. We show how to combine index compilation with previous approaches, such as binary tree search, cache-sensitive tree search, and the architecture-sensitive tree search presented by Kim et al.
Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation
Venue: arXiv cs.CL 2017
Year: 2017

Natural language generation (NLG) is an important component in spoken dialogue systems. This paper presents a model called Encoder-Aggregator-Decoder which is an extension of an Recurrent Neural Network based Encoder-Decoder architecture. The proposed Semantic Aggregator consists of two components: an Aligner and a Refiner. The Aligner is a conventional attention calculated over the encoded input information, while the Refiner is another attention or gating mechanism stacked over the attentive Aligner in order to further select and aggregate the semantic elements. The proposed model can be jointly trained both sentence planning and surface realization to produce natural language utterances. The model was extensively assessed on four different NLG domains, in which the experimental results showed that the proposed generator consistently outperforms the previous methods on all the NLG domains.
Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognition
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a new unsupervised learning approach with stacked autoencoder (SAE) for Arabic handwritten digits categorization. Recently, Arabic handwritten digits recognition has been an important area due to its applications in several fields. This work is focusing on the recognition part of handwritten Arabic digits recognition that face several challenges, including the unlimited variation in human handwriting and the large public databases. Arabic digits contains ten numbers that were descended from the Indian digits system. Stacked autoencoder (SAE) tested and trained the MADBase database (Arabic handwritten digits images) that contain 10000 testing images and 60000 training images. We show that the use of SAE leads to significant improvements across different machine-learning classification algorithms. SAE is giving an average accuracy of 98.5%.
Variable-to-Fixed Length Homophonic Coding Suitable for Asymmetric Channel Coding
Venue: arXiv cs.IT 2017
Year: 2017

In communication through asymmetric channels the capacity-achieving input distribution is not uniform in general. Homophonic coding is a framework to invertibly convert a (usually uniform) message into a sequence with some target distribution, and is a promising candidate to generate codewords with the nonuniform target distribution for asymmetric channels. In particular, a Variable-to-Fixed length (VF) homophonic code can be used as a suitable component for channel codes to avoid decoding error propagation. However, the existing VF homophonic code requires the knowledge of the maximum relative gap of probabilities between two adjacent sequences beforehand, which is an unrealistic assumption for long block codes. In this paper we propose a new VF homophonic code without such a requirement by allowing one-symbol decoding delay. We evaluate this code theoretically and experimentally to verify its asymptotic optimality.
Maxent-Stress Optimization of 3D Biomolecular Models
Venue: arXiv cs.DS 2017
Year: 2017

Knowing a biomolecule's structure is inherently linked to and a prerequisite for any detailed understanding of its function. Significant effort has gone into developing technologies for structural characterization. These technologies do not directly provide 3D structures; instead they typically yield noisy and erroneous distance information between specific entities such as atoms or residues, which have to be translated into consistent 3D models. Here we present an approach for this translation process based on maxent-stress optimization. Our new approach extends the original graph drawing method for the new application's specifics by introducing additional constraints and confidence values as well as algorithmic components. Extensive experiments demonstrate that our approach infers structural models (i. e., sensible 3D coordinates for the molecule's atoms) that correspond well to the distance information, can handle noisy and error-prone data, and is considerably faster than established tools. Our results promise to allow domain scientists nearly-interactive structural modeling based on distance constraints.
Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification
Venue: arXiv cs.SD 2017
Year: 2017

Music tag words that describe music audio by text have different levels of abstraction. Taking this issue into account, we propose a music classification approach that aggregates multi-level and multi-scale features using pre-trained feature extractors. In particular, the feature extractors are trained in sample-level deep convolutional neural networks using raw waveforms. We show that this approach achieves state-of-the-art results on several music classification datasets.
Decomposition of Trees and Paths via Correlation
Venue: arXiv cs.DM 2017
Year: 2017

We study the problem of decomposing (clustering) a tree with respect to costs attributed to pairs of nodes, so as to minimize the sum of costs for those pairs of nodes that are in the same component (cluster). For the general case and for the special case of the tree being a star, we show that the problem is NP-hard. For the special case of the tree being a path, this problem is known to be polynomial time solvable. We characterize several classes of facets of the combinatorial polytope associated with a formulation of this clustering problem in terms of lifted multicuts. In particular, our results yield a complete totally dual integral (TDI) description of the lifted multicut polytope for paths, which establishes a connection to the combinatorial properties of alternative formulations such as set partitioning.